Methods, systems, and media for language identification of a media content item based on comments

ABSTRACT

Methods, systems, and media for language identification of a media content item based on comments are provided. In some embodiments, the method includes: obtaining a plurality of comments associated with a media content item; selecting a subset of the plurality of comments based on one or more criteria; assigning, for each comment in the subset of the plurality of comments, a vector of language probabilities, wherein each component of the vector is assigned a language probability that indicates the likelihood that the comment includes content in a language from a plurality of languages; combining the vector of language probabilities for each comment in the subset of the plurality of comments to generate a combined language vector; identifying a language associated with the media content item based on the combined language vector; and performing an action based on the identified language.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/322,685, filed Apr. 14, 2016, which is herebyincorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosed subject matter relates to methods, systems, and media forlanguage identification of a media content item based on comments.

BACKGROUND

Many users access video content from services having large collectionsof video content items. Frequently, these collections include videocontent that has been uploaded by users from various countries and thatcontains audio content and/or text content in a variety of languages. Assuch, video content may be served to users that are unlikely tocomprehend the content. For some video content, it may be important forthese services to present users with video content that contain audioand/or text content in a language that the user can comprehend.

These services, however, often rely on information that may or may notcorrectly identify a language used in the video content, such asinformation in metadata or information provided by a user that uploadedthe video content. Moreover, in many instances, the language associatedwith the video content has not been indicated by the user that uploadedthe video content. Techniques, such as automatic speech recognition(ASR), may sometimes be used to determine a language of the videocontent. Such recognition techniques, however, are not supported for alllanguages and have problems with background music, noise and multi-partyconversations, etc. in the video content. Thus, it is difficult toidentify the language of video content.

Accordingly, it is desirable to provide new methods, systems, and mediafor language identification of a media content item based on comments.

SUMMARY

In accordance with some implementations of the disclosed subject matter,mechanisms for language identification of a media content item based oncomments are provided.

In accordance with some implementations of the disclosed subject matter,a method for language identification of media content is provided, themethod comprising: obtaining a plurality of comments associated with amedia content item; selecting a subset of the plurality of commentsbased on one or more criteria; assigning, for each comment in the subsetof the plurality of comments, a vector of language probabilities,wherein each component of the vector is assigned a language probabilitythat indicates the likelihood that the comment includes content in alanguage from a plurality of languages; combining the vector of languageprobabilities for each comment in the subset of the plurality ofcomments to generate a combined language vector; identifying a languageassociated with the media content item based on the combined languagevector; and performing an action based on the identified language.

In some embodiments, selecting the subset of the plurality of commentsbased on one or more criteria includes removing comments that do notmeet a predetermined number of words or a predetermined number ofcharacters.

In some embodiments, the method further comprises determining a lengthof each comment in the subset of the plurality of comments, wherein thecombined language vector is a weighted average of the languageprobabilities for each of the plurality of languages and across thesubset of the plurality of comments that is weighted based on thedetermined length of each comment.

In some embodiments, the method further comprises determining a votingindication associated with each comment in the subset of the pluralityof comments, wherein the combined language vector is a weighted averageof the language probabilities for each of the plurality of languages andacross the subset of the plurality of comments that is weighted based onthe determined voting indication.

In some embodiments, determining a language associated with the mediacontent item based on the combined language vector further comprisesaugmenting the combined language vector with an additional vector oflanguage probabilities corresponding to metadata associated with themedia content item.

In some embodiments, determining a language associated with the mediacontent item based on the combined language vector further comprisesaugmenting the combined language vector with media content iteminformation. In some embodiments, the media content item informationincludes a category of the media content item.

In some embodiments, performing the action further comprises presentingone or more related media content items in the identified language inresponse to presenting the media content item.

In some embodiments, performing the action further comprises:transmitting information corresponding to the identified language to anadvertisement server; receiving, from the advertisement server, anadvertisement that corresponds to the identified language; and causingthe advertisement to be presented.

In some embodiments, performing the action further comprises:determining that a second media content item to be presented has alanguage identifier that is different than the identified language; andpresenting subtitle information during the presentation of the secondmedia content item, wherein the subtitle information is in theidentified language.

In accordance with some implementations of the disclosed subject matter,a system for language identification of media content is provided, thesystem comprising a hardware processor that is configured to: obtain aplurality of comments associated with a media content item; select asubset of the plurality of comments based on one or more criteria;assign, for each comment in the subset of the plurality of comments, avector of language probabilities, wherein each component of the vectoris assigned a language probability that indicates the likelihood thatthe comment includes content in a language from a plurality oflanguages; combine the vector of language probabilities for each commentin the subset of the plurality of comments to generate a combinedlanguage vector; identify a language associated with the media contentitem based on the combined language vector; and perform an action basedon the identified language.

In accordance with some implementations of the disclosed subject matter,a non-transitory computer-readable medium containing computer executableinstructions that, when executed by a processor, cause the processor toperform a method for language identification of media content isprovided, the method comprising: obtaining a plurality of commentsassociated with a media content item; selecting a subset of theplurality of comments based on one or more criteria; assigning, for eachcomment in the subset of the plurality of comments, a vector of languageprobabilities, wherein each component of the vector is assigned alanguage probability that indicates the likelihood that the commentincludes content in a language from a plurality of languages; combiningthe vector of language probabilities for each comment in the subset ofthe plurality of comments to generate a combined language vector;identifying a language associated with the media content item based onthe combined language vector; and performing an action based on theidentified language.

In accordance with some implementations of the disclosed subject matter,a system for language identification of media content is provided, thesystem comprising: means for obtaining a plurality of commentsassociated with a media content item; means for selecting a subset ofthe plurality of comments based on one or more criteria; means forassigning, for each comment in the subset of the plurality of comments,a vector of language probabilities, wherein each component of the vectoris assigned a language probability that indicates the likelihood thatthe comment includes content in a language from a plurality oflanguages; means for combining the vector of language probabilities foreach comment in the subset of the plurality of comments to generate acombined language vector; means for identifying a language associatedwith the media content item based on the combined language vector; andmeans for performing an action based on the identified language.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features, and advantages of the disclosed subjectmatter can be more fully appreciated with reference to the followingdetailed description of the disclosed subject matter when considered inconnection with the following drawings, in which like reference numeralsidentify like elements.

FIG. 1 shows an illustrative example of a user interface for presentingvideo content and comments associated with the presented video contentin accordance with some embodiments of the disclosed subject matter.

FIG. 2 shows a schematic diagram of an illustrative system suitable forimplementation of the mechanisms described herein for languageidentification of a media content item based on comments in accordancewith some embodiments of the disclosed subject matter.

FIG. 3 shows a detailed example of hardware that can be used in a serverand/or a user device of FIG. 2 in accordance with some embodiments ofthe disclosed subject matter.

FIG. 4 shows an illustrative example of a process for determining thelanguage of a media content item based on comments in accordance withsome embodiments of the disclosed subject matter.

DETAILED DESCRIPTION

In accordance with various embodiments of the disclosed subject matter,mechanisms (which can include methods, systems, and media) for languageidentification of a media content item based on comments are provided.

In some embodiments of the disclosed subject matter, the mechanismsdescribed herein can retrieve comments that are associated with a mediacontent item. These comments can be ranked and/or filtered in anysuitable manner (e.g., by length, by popularity, by user engagement, byany other suitable manner, or any suitable combination thereof). Forexample, this can include filtering out comments that are less than fivewords or less than twenty characters in length (e.g., to remove shortnon-meaningful comments), filtering out automatically-generated comments(e.g., a “Shared on this Service” comment), filtering out comments thatare longer than ten thousand characters in length (e.g., to removemachine-generated comments), and/or filtering out comments that includeURLs and no text content.

It should be noted that the retrieved comments can include comments thatare publicly accessible. It should also be noted that a comment can, insome instances, be deleted such that the comment is no longer associatedwith the media content item. The language identification mechanismsdescribed herein can retrieve an updated set of comments (e.g., from acomments database associated with a media service) such that deletedcomments are excluded from consideration.

Upon selecting a subset of comments, the mechanisms can generate avector of probabilities for each of multiple languages for eachcomments. For each comment in the subset of selected comments, themechanisms can, for example, generate a vector of probabilities, whereeach component of the vector is a probability that a given comment is ina particular language (e.g., one component of the vector can include a0.5 probability score that the comment is in the Spanish language).

In some embodiments, the vectors for each of the comments in the subsetof comments can be combined to generate a single vector of probabilitiesfor each of multiple language in the subset of comments. Combining thevectors of language probabilities can include, for example, averagingthe language probability scores for a particular language across thevectors for each comment to obtain a language score for a particularlanguage that is placed in the single vector of language scores for themedia content item across the subset of comments. It should be notedthat, in some embodiments, when combining the vectors of languageprobabilities, one or more weights can be applied to the languageprobability scores in each vector. For example, when combining thevectors of probabilities for each of the comments, a weight can beapplied that takes into account the length of the comment. In a moreparticular example, when determining a weighted average language scorefor the set of comments, the weight of the scores for each comment canbe proportional to the length of the comment. As such, weights can beused to emphasize that longer comments can bear more weight ondetermining the language of the media content item.

In response to generating a combined vector of language probabilitiesfor the subset of comments associated with a media content item, themechanisms can determine a language to associate with the media contentitem. This can, for example, include selecting the language associatedwith the maximum score in the combined vector language probabilitiesafter averaging across all of the comments in the subset of comments.

For example, a music video can be uploaded to a social media platform, avideo provision service, or any other suitable service by a user thatspeaks English, and consequently has a title and/or other inputtedmetadata that is also in English, but contains audio content and textcontent that is in Spanish. In response to generating a vector oflanguage probabilities for each comment associated with the music video,combining the vectors for each of the comments to generate a singlevector across the comments, and determining which component of thesingle vector has the highest score (e.g., the component thatcorresponds to the Spanish language), these mechanisms can set alanguage identifier of the music video to the Spanish language.

It should be noted that, in some embodiments, the language informationderived from the comments associated with the media content item can beaugmented or supplemented with additional information (e.g., descriptionmetadata, title metadata, upvotes associated with the media contentitem, user information, etc.) to determine the language associated withthe media content item.

In some embodiments, upon associating a language identifier with themedia content item (e.g., that the music video contains content that isin the Spanish language), the mechanisms can perform additional actionsusing the determined language identifier. For example, the mechanismscan present recommendation interfaces that include additional mediacontent items that also have the same language identifier (e.g., othermusic videos having the Spanish language identifier). In anotherexample, the mechanisms can present search results that are responsiveto a search query, where the search results have the same languageidentifier (e.g., video content having the Spanish language identifier).In yet another example, the mechanisms can transmit the languageidentifier to other sources to obtain additional content, such asadvertising content (e.g., advertisements that are in Spanish). In afurther example, the mechanisms can be used to provide subtitleinformation or other supplemental information in the determined language(e.g., Spanish subtitles for video content that is not in Spanish).

In some embodiments, upon performing the comment-based languageidentification on multiple media content items (e.g., each of the videosin a video database), the mechanisms can use the language identifierassociated with each of the media content items along with informationrelating to the user to present media content recommendations to theuser associated with the user account. For example, in response toreceiving an indication that the user of a media application prefersmedia content in a particular language (e.g., from a language preferenceindicator), the mechanisms can use the language identifier associatedwith each of the media content items to recommend and/or promote mediacontent having a language identifier that matches the languagepreference indicator of the user. In another example, the mechanisms candetermine information about the user (e.g., that the user speaksSpanish). Such information about the user can be determined by, forexample, detecting that a user has accessed a media service using a useraccount and, in response to receiving affirmative consent to review useraccount information, access user account information (e.g., userlanguage preferences). In response to determining this information aboutthe user, the mechanisms can recommend particular media content items tothe user. For example, in response to determining that the user hasentered search terms into a media searching interface, the mechanismscan obtain the search results that are responsive to the search termsand re-rank the search results using the associated languageidentifiers. In continuing this example, search results having languageidentifiers that correspond to the information about the user (e.g.,videos with Spanish language identifiers for a user that speaks Spanish)can be promoted to the top of a list of recommended media content items.

It should be noted that, although the mechanisms described hereingenerally relate to language identification of a video content itembased on comments that have been provided in relation to that videocontent item, the mechanisms can be used to identify a language of thecontent in any suitable type of media content, such as audio content(e.g., music, radio programs, audiobooks, and/or any other suitable typeof audio content), television programs, movies, live streaming mediacontent, electronic books, and/or any other suitable type of mediacontent.

Turning to FIG. 1, an example 100 of a user interface for presenting amedia content item and comments is illustrated in accordance with someembodiments of the disclosed subject matter. As illustrated, in someembodiments, user interface 100 can include a video display element 110,a video content item 112, a search element 114, comments 122, 124, and126 which are associated with user representation identifiers 116, 118,and 120, and one or more pieces of metadata 128.

Video display element 110 can be any suitable video display element. Insome embodiments, video display element can be configured to display anysuitable video format, such as FLASH, AVI, MP4, and/or any othersuitable video format. In some embodiments, video display element 110can present video content item 112 in a video player window which caninclude any suitable controls, such as a pause control, a volumecontrol, rewind and/or fast-forward controls, and/or any other suitablecontrols.

Additionally or alternatively, in some embodiments, user interface 100can be configured to include any suitable type of media player. Forexample, user interface 100 can include a media player suitable forplaying audio files, image files, any other suitable media contentitems, and/or any suitable combination thereof.

Video content item 112 can be any suitable video content item. In someembodiments, video content item 112 can include verbal content and/ortextual content in any suitable language. For example, video contentitem 112 can be a video that includes verbal content and/or textualcontent in English. In another example, video content item 112 can be avideo that includes content in multiple languages, such as verbalcontent in Spanish and textual content in English. It should be notedthat, in some embodiments, video content item 112 may include no verbalcontent and/or no textual content (such as silent videos).

Comments 122, 124, and 126 can be any suitable comments. For example,comments can include text, audio data, video data, and/or image datathat provides an opinion of, or otherwise remarks upon, the contents ofa media content item or a portion of a media content item. In a moreparticular example, as shown in FIG. 1, each of the comments 122, 124,and 126 can include textual content posted by a user in connection withmedia content item 112 or a portion of media content item 112. Inanother more particular example, the comments can include one or moreposts written and submitted by a user associated with a social mediaplatform that is visible by one or more users having an establishedrelationship with the user via the social media platform. In such anexample, the post on a social media platform can include commentary thatreferences or includes media content item 112. In yet another moreparticular example, the comments can be aggregated or otherwise obtainedfrom multiple sources, such as comments relating to media content item112 that are received from users via commenting functionality associatedwith a browsing application installed on a client device, commentsrelating to media content item 112 that are received from usersassociated with a social media platform or any other suitable sharingservice, etc.

In some embodiments, each of comments 122, 124, and 126 can beassociated with a particular user account. For example, as illustratedin FIG. 1, comments 122, 124, and 126 can be associated with useraccounts that are identified by representations 116, 118, and 120. Insuch an example, representations 116, 118, and 120 can be associatedwith a user account of a social media platform, a user account of amedia content platform, a user account of an e-mail server, or a user ofany other suitable service. Alternatively, in some embodiments, each ofcomments 122, 124, 126 can be associated with a non-particular oranonymous user. In some embodiments, each of comments 122, 124, and 126can be associated with no user accounts.

In a more particular example, a record for each of these comments in acomments database can include a comment identifier that identifies thecomment itself, an author identifier that identifies a user accountassociated with a user or a group of users, a representation of theauthor that created, modified, and/or posted the comment (e.g.,representations 116, 118, and 120 that are each associated with useraccounts), a timestamp that indicates the time when the comment wascreated, the content of the comment, a media content identifier thatidentifies the media content item or a portion of the media content itemrelating to the comment, etc. Note that, although three comments areshown in FIG. 1 and are described herein, any suitable number (e.g.,one, two, four, ten, and/or any other suitable number) of comments canbe included.

It should be noted that, comments, such as comments 122, 124, and 126,can be in any suitable language. For example, as illustrated in FIG. 1,comments 122 and 124 include textual content that is in Spanish whilecomment 126 includes textual content that is in English. In anotherexample, comments can include textual content that is in multiplelanguages (e.g., a text post that is written in both English andSpanish). In yet another example, comments can include textual contentthat is in one language and image data that includes content in anotherlanguage.

In some embodiments, the mechanisms described herein can determinelanguage probability scores of the comments associated with a mediacontent item and associate the media content item with a languageidentifier based on the language probability scores of the comments. Forexample, as illustrated in FIG. 1, the mechanisms can generate a vectorof language probabilities for comments 122, 124, and 126, where theSpanish language components of the vectors for comments 122 and 124 havea greater score than the Spanish language component of the vector forcomment 126 and the English language component of the vector for comment126 has a greater score than the English language components of thevectors for comments 122 and 124. In such an example, because theSpanish language component of a single vector that combines the vectorsfor comments 122, 124, and 126 has the maximum value among the othercomponents of the single vector, the mechanisms can associate mediacontent item 112 with a language identifier that identifies the languageof the media content item as the Spanish language. It should be notedthat the language information derived from the single vector can also beused with additional indicators (e.g., title metadata, descriptionmetadata, user account information, etc.) to determine a languageassociated with media content item 112.

In continuing with this example, upon associating the media content itemwith a language identifier that identifies the language of the mediacontent item as the Spanish language, the mechanisms can perform anysuitable action, such as present other media content items having thesame language identifier (e.g., in response to requesting arecommendation for additional media content items, in response toentering a search query into search element 114 of FIG. 1, etc.),present an advertisement in the same language as the language identifierassociated with the media content item (e.g., a video advertisementpresented in video display element 110 before, during, or after thepresentation of media content item 112), etc.

Turning to FIG. 2, an example 200 of hardware that can be used inaccordance with some embodiments of the disclosed subject matter forlanguage identification of a media content item based on comments isshown. As illustrated, hardware 200 can include one or more servers,such as a content server 202 and a data server 204, as well as acommunication network 210, and/or one or more user devices 212, such asuser devices 214 and 216.

In some embodiments, content server 202 can be any suitable server forstoring media content and delivering the content to a user device 212.For example, content server 202 can be a server that streams mediacontent to a user device 212 via communication network 210. Mediacontent provided by content server 202 can be any suitable content, suchas video content, audio content, electronic books, documents, images,and/or any other suitable type of media content. As a more particularexample, media content can include television programs, movies,cartoons, sound effects, streaming live content (e.g., a streaming radioshow, a live concert, and/or any other suitable type of streaming livecontent), and/or any other suitable type of media content. Media contentcan be created and uploaded to content server 202 by any suitableentity. In some embodiments, content server 202 can be omitted.

In some embodiments, data server 204 can be any suitable server forstoring and/or transmitting information related to one or more mediacontent items. As a more particular example, in some embodiments, dataserver 204 can store and/or transmit metadata that is associated with amedia content item. As another more particular example, in someembodiments, data server 204 can include a comments database that storesinformation related to comments. For example, as described above, arecord for a comment in the comments database can include a commentidentifier that identifies the comment itself, an author identifier thatidentifies a user account associated with a user or a group of users, arepresentation of the author that created, modified, and/or posted thecomment, a timestamp that indicates the time when the comment wascreated, the content of the comment, a media content identifier thatidentifies the media content item or a portion of the media content itemrelating to the comment, etc. In some embodiments, data server 204 canbe omitted.

Communication network 210 can be any suitable combination of one or morewired and/or wireless networks in some embodiments. For example,communication network 210 can include any one or more of the Internet,an intranet, a wide-area network (WAN), a local-area network (LAN), awireless network, a digital subscriber line (DSL) network, a frame relaynetwork, an asynchronous transfer mode (ATM) network, a virtual privatenetwork (VPN), and/or any other suitable communication network. Userdevices 212 can be connected by one or more communications links 218 tocommunication network 210 which can be linked via one or morecommunications links (e.g., communications links 220 and/or 222) tocontent server 202, application and data server 204, advertisementserver 206, and payment server 208. Communications links 218, 220,and/or 222 can be any communications links suitable for communicatingdata among user devices 212 and servers 202 and/or 204 such as networklinks, dial-up links, wireless links, hard-wired links, any othersuitable communications links, or any suitable combination of suchlinks.

User devices 212 can include any one or more user devices suitable forrequesting media content, searching for media content, presenting mediacontent, presenting advertisements, receiving input for playing mediacontent and/or any other suitable functions. For example, in someembodiments, a user device 212 can be implemented as a mobile device,such as a mobile phone, a tablet computer, a laptop computer, a vehicle(e.g., a car, a boat, an airplane, or any other suitable vehicle)entertainment system, a portable media player, and/or any other suitablemobile device. As another example, in some embodiments, a user device212 can be implemented as a non-mobile device such as a desktopcomputer, a set-top box, a television, a streaming media player, a gameconsole, and/or any other suitable non-mobile device.

Although content server 202 and data server 204 are illustrated asseparate devices, the functions performed by content server 202 and dataserver 204 can be performed using any suitable number of devices in someembodiments. For example, in some embodiments, the functions performedby either content server 202 or data server 204 can be performed on asingle server. As another example, in some embodiments, multiple devicescan be used to implement the functions performed by content server 202and data server 204.

Although two user devices 214 and 216 are shown in FIG. 2 to avoidover-complicating the figure, any suitable number of user devices,and/or any suitable types of user devices, can be used in someembodiments.

Content server 202, data server 204, and user devices 212 can beimplemented using any suitable hardware in some embodiments. Forexample, in some embodiments, devices 202, 204, and 212 can beimplemented using any suitable general purpose computer or specialpurpose computer. As another example, a mobile phone may be implementedusing a special purpose computer. Any such general purpose computer orspecial purpose computer can include any suitable hardware. For example,turning to FIG. 3, as illustrated in example hardware 300, such hardwarecan include hardware processor 302, memory and/or storage 304, an inputdevice controller 306, an input device 308, display/audio drivers 310,display/audio output circuitry 312, communication interface(s) 314, anantenna 316, and a bus 318.

Hardware processor 302 can include any suitable hardware processor, suchas a microprocessor, a micro-controller, digital signal processor(s),dedicated logic, and/or any other suitable circuitry for controlling thefunctioning of a general purpose computer or a special purpose computerin some embodiments. In some embodiments, hardware processor 302 can becontrolled by a server program stored in memory and/or storage 304 of aserver (e.g., such as one of servers 202 or 204). For example, theserver program can cause hardware processor 302 to perform themechanisms described herein for language identification of a mediacontent item based on comments and/or perform any other suitableactions. In some embodiments, hardware processor 302 can be controlledby a computer program stored in memory and/or storage 304 of a userdevice 212. For example, the computer program can cause hardwareprocessor 302 to present a media content item, request a media contentitem, and/or perform the mechanisms described herein for languageidentification of a media content item based on comments.

Memory and/or storage 304 can be any suitable memory and/or storage forstoring application information, programs, data, media content, and/orany other suitable information in some embodiments. For example, memoryand/or storage 304 can include random access memory, read-only memory,flash memory, hard disk storage, optical media, and/or any othersuitable memory.

Input device controller 306 can be any suitable circuitry forcontrolling and receiving input from one or more input devices 308 insome embodiments. For example, input device controller 306 can becircuitry for receiving input from a touchscreen, from a keyboard, froma mouse, from one or more buttons, from a voice recognition circuit,from a microphone, from a camera, from an optical sensor, from anaccelerometer, from a temperature sensor, from a near field sensor,and/or from any other type of input device.

Display/audio drivers 310 can be any suitable circuitry for controllingand driving output to one or more display/audio output devices 312 insome embodiments. For example, display/audio drivers 310 can becircuitry for driving a touchscreen, a flat-panel display, a cathode raytube display, a projector, a speaker or speakers, and/or any othersuitable display and/or presentation devices.

Communication interface(s) 314 can be any suitable circuitry forinterfacing with one or more communication networks, such as network 210as shown in FIG. 2. For example, interface(s) 314 can include networkinterface card circuitry, wireless communication circuitry, and/or anyother suitable type of communication network circuitry.

Antenna 316 can be any of one or more suitable antennas for wirelesslycommunicating with a communication network (e.g., communication network210) in some embodiments. In some embodiments, antenna 316 can beomitted.

Bus 318 can be any suitable mechanism for communicating between two ormore components 302, 304, 306, 310, and 314 in some embodiments.

Any other suitable components can be included in hardware 300 inaccordance with some embodiments.

Turning to FIG. 4, an example 400 of a process for languageidentification of a media content item based on comments is shown inaccordance with some embodiments of the disclosed subject matter. Insome embodiments, process 400 can be executed by any device orcombination of devices. For example, with reference to FIG. 2, process400 can be executed by content server 202, data server 204, and/or userdevice 212.

In some embodiments, process 400 can begin at 402 by retrieving commentsassociated with a media content item using any suitable techniques orcombination of techniques. For example, comments that are associatedwith a media content item (e.g., video 112 in FIG. 1) can be received ata server device (e.g., server device 202, as described above inconnection with FIG. 2) when submitted by a user of a user device (e.g.,user device 204 as described above in connection with FIG. 2). In suchan example, process 400 can access, request, and/or retrieve thecomments from the server device. In a more particular example, inresponse to transmitting a request that includes a media contentidentifier that identifies the media content item or a portion of themedia content item, the server device can access comment and associatedcomment information from a comments database. The comment informationfrom the comments database can include a comment identifier thatidentifies the comment, an author identifier that identifies a useraccount associated with a user or a group of users, a representation ofthe author that created, modified, and/or posted the comment (e.g.,representations 116, 118, and 120 that are each associated with useraccounts), a timestamp that indicates the time when the comment wascreated, the content of the comment, a media content identifier thatidentifies the media content item or a portion of the media content itemrelating to the comment, etc. As another example, comments can beincluded in the metadata associated with the media content item. In suchan example, process 400 can access, request, and/or retrieve thecomments associated with the media content item concurrently with themetadata associated with the media content item (e.g., the title of themedia content item, a description of the media content item, the contentcreator of the media content item, etc.).

It should be noted that the retrieved comments can includeuser-generated comments that are publicly accessible. For example, aprivacy indicator can indicate that a comment was posted publicly suchthat the comment is accessible by users not having a particularrelationship with the user that provided the comment. It should also benoted that a comment can, in some instances, be deleted such that thecomment is no longer associated with the media content item. Thelanguage identification mechanisms described herein can transmit arequest that retrieves an updated set of comments (e.g., from thecomments database) such that deleted comments are excluded fromconsideration.

It should be noted that the media content item can be a publiclyaccessible media content item. For example, a privacy indicator canindicate that a video content item was uploaded to a video sharingservice in which the video content item is accessible by users nothaving a particular relationship with the user that uploaded the videocontent item. In another example, process 400 can determine that thevideo content item uploaded by the user was not associated withparticular access controls (e.g., only viewable by users in a particularsocial circle, only viewable by a particular user, etc.).

In some embodiments, process 400 can select a subset of comments fromthe retrieved comments at 404 using any suitable technique orcombination of techniques, and using any suitable information orcombination of information.

In some embodiments, process 400 can select a subset of comments byfiltering out comments based on particular criterion. For example,process 400 can filter out comments from the retrieved set of commentsthat are less than a particular threshold length—e.g., less than aparticular number of words (e.g., five words) or less than a particularnumber of characters (e.g., twenty characters). In this example,comments like “OK” or “MERCI” can be removed as unlikely to provide alanguage indicator for the associated media content item, where thecomment like “OK” is generally used in many languages and the comment“MERCI” does not necessarily indicate that the commentator is capable ofspeaking Spanish. As such, filtering by the length of the comment can,for example, remove short and, in some instances, non-meaningfulcomments from consideration. In a more particular example, referringback to FIG. 1, process 400 can determine that comment 122 containstwenty-four characters, comment 124 contains thirty-two characters, andcomment 126 contains seven characters, and, upon applying a thresholdvalue of at least twenty characters, process 400 can filter the commentsby removing comment 126 and placing comments 122 and 124 into a subsetof selected comments for analysis.

Similarly, process 400 can obtain user-generated comments by filteringout comments from the retrieved set of comments that are greater than aparticular threshold length—e.g., greater than ten thousand charactersin length. This type of filtering criterion can, for example, removemachine-generated comments that tend to be lengthy. In another example,process 400 can filter out comments that have been deemed abusive ormoderated. In yet another example, process 400 can obtain user-generatedcomments by filtering out automatically-generated comments (e.g., a“Shared on this Service” comment or “Sent by my device” comment). In afurther example, process 400 can filter out comments that substantiallyinclude or only include URLs and little to no text content.

It should be noted that any suitable filtering criterion or filteringrules for removing comments from consideration can be used.

Additionally or alternatively, in some embodiments, process 400 canselect comments for placement in the subset of comments based at leastin part on user information associated with each comment. For example,process 400 can select comments based on information contained in a userprofile or a user account associated with each comment. In a moreparticular example, process 400 can select comments from the set ofretrieved comments based on the number of other comments made by orassociated with the user account, a number of followers associated withthe user account that made the comment, whether the user account isverified, whether the user account is a member of a group of usersassociated with the media content item (e.g., if a media content itemhas been posted or uploaded by a member of a group and the user accountcorresponding to the comment is also a member of the group), whether theuser account is associated with the source of video (e.g., if a mediacontent item has been posted or uploaded by a user account that is afollower or subscriber of the user account associated with the comment),any other information associated with a user account, and/or anysuitable combination thereof.

It should be noted that, prior to retrieving comments associated with amedia content item and prior to accessing user account information,these mechanisms can provide a user with an opportunity to provideaffirmative consent and/or authorization to access and analyze commentinformation and user account information, such as receiving userhistorical information (e.g., browsing history, commenting history,etc.) and/or user preferences. For example, upon loading an applicationfor playing media content on a computing device, such as a mobiledevice, such an application can prompt the user to provide the consentand/or authorization. In a more particular example, in response todownloading the application for playing media content and loading theapplication on the computing device, the user can be prompted with amessage that requests (or requires) that the user provide consent and/orauthorization for the mechanisms to access and/or analyze user accountinformation.

Additionally or alternatively, in some embodiments, process 400 canselect comments for placement in the subset of comments based at leastin part on popularity information associated with each comment. Forexample, process 400 can select comments based on the number of upvotes(e.g., thumbs up), likes, and/or any other received indication ofapproval (e.g., the top ten comments based on number of upvotes). Asanother example, process 400 can select comments based on the totalnumber of views of each comment (e.g., the top ten comments based onnumber of views). As yet another example, process 400 can selectcomments based on the number of sub-comments posted in connection witheach comment.

Referring back to FIG. 4, process 400 can continue at 406 by assigning avector of language probabilities for each comment in the subset ofcomments. Each vector of language probabilities for each comment caninclude multiple vector components, where each component includes aprobability score that indicates the likelihood that the commentincludes content that is in a particular language selected from multiplelanguages. It should be noted that the number of components can be basedat least in part on the number of languages (e.g., five languagecomponents, one hundred language components, etc.).

For example, the vector of language probabilities for a comment C₁ canbe represented as (L₁, L₂, . . . , L_(i)), where each L_(i) is aprobability score that comment C₁ includes content that is in languagei. Such a vector can be generated for each comment in the subset ofselected comments.

In a more particular example, process 400 can classify each comment inthe subset of comments using a machine language classifier, such as anaïve Bayes classifier, a maximum entropy classifier, a random forestclassifier, any other language identification technique, and/or anysuitable combination thereof. Using such a machine language classifier,process 400 can analyze each comment and return a probability score foreach language that the machine language classifier is trained to detect(e.g., five languages, one hundred languages, etc.).

In connection with FIG. 1, process 400 can, for a subset of rankedcomments (e.g., the top N ranked comments based on length andpopularity), use a machine learning language classifier to determine alanguage probability score for each of a set of known languages. Forexample, process 400 can determine, for comment 122, that the Spanishlanguage probability score is 0.8, that the English language probabilityscore is 0.1, and that the French language probability score is 0.05,and that the Japanese language probability score is 0.05. In such anexample, process 500 can generate a language probability vector L of[0.8, 0.1, 0.05, 0.05].

It should be noted that, in response to the machine language classifierfailing to identify a language of a comment, process 400 can furtherfilter the subset of comments by removing that comment for languageidentification and/or use in training dataset. This can, for example,filter out comments that include content in a rare language and/orcomments that include non-text content (such as emoticons).

Referring back to FIG. 4, upon obtaining vectors of languageprobabilities for each of the subset of comments, process 400 cancontinue at 408 by combining the vectors of language probabilities togenerate a single vector for the media content item (e.g., the videoshown in FIG. 1). Combining the vectors of language probabilities caninclude, for example, averaging the language probability scores for aparticular language across the vectors for each comment to obtain alanguage score for a particular language that is placed in the singlevector of language scores for the media content item across the subsetof comments.

In some embodiments, this combination of vectors can be based on anysuitable weight applied to the vector of language probabilities for eachcomment. For example, if comment C₁ is represented as (L₁₁, L₁₂, . . . ,L_(1n)) and comment C₂ is represented as (L₂₁, L₂₂, . . . , L_(2n)),where n is the number of languages, a weight w can be used to combinethe vector for comment C₁ and comment C₂. This weighted average can berepresented as (w₁/(w₁+w₂))*L₁₁+(w₂/(w₁+w₂))*L₂₁,(w₁/(w₁+w₂))*L₁₂+(w₂/(w₁+w₂))*L₂₂, and so on, where w₁ is the weight forcomment C₁ and w₂ is the weight for comment C₂. As described herein, theweight can be any suitable weight, such as the length of each comment,the number of upvotes associated with each comment, etc.

In some embodiments, the weight can be based on the length of eachcomment, such as the number of words in a comment or the number ofcharacters in a comment. In this example, the language probabilityscores in a vector for longer comments having a greater number of wordsor a greater number of characters can be weighted higher than thelanguage probability scores in a vector for shorter comments. Forexample, the length m for each comment can be determined and used tocombine the vectors of language probabilities, which can be representedas (m₁/(m₁+m₂))*L₁₁+(m₂/(m₁+m₂))*L₂₁, (m₁/(m₁+m₂))*L₁₂+(m₂/(m₁+m₂))*L₂₂,and so on, where m₁ is the length of comment C₁ and m₂ is the length ofcomment C₂. In a more particular example, referring back to FIG. 1, inresponse to determining that comment 122 has a length of five words,comment 124 has a length of six words, and comment 126 has a length ofone word in the subset of selected comments associated with mediacontent item 112, the language probability scores in the vectorrepresenting comment 124 can be weighted more heavily than the languageprobability scores in the vector representing comment 122, which can beweighted more heavily than the language probability scores in the vectorrepresenting comment 126.

Accordingly, upon weighing the language probability scores for eachcomment by the length of that comment, process 400 can generate a vectorof weighted average language scores across the subset of selectedcomments for the media content item. That is, when averaging languageprobability scores, the weight of the language probability scores foreach comment is proportional to the length of that comment. This can,for example, generate a vector of sparse continuous features thatrepresent the weighted average scores across the subset of selectedcomments of the media content item.

In some embodiments, the weight can be based on other informationrelating to each comment, such as popularity information associated witheach comment. Popularity information can include, for example, thenumber of upvotes (e.g., thumbs up), likes, and/or any other receivedindication of approval. In this example, the language probability scoresin a vector for comments that received a greater number of upvotes canbe weighted higher than the language probability scores in a vector forshorter comments. For example, the number of upvotes v for each commentcan be determined and used to combine the vectors of languageprobabilities, which can be represented as(v₁/(v₁+v₂))*L₁₁+(v₂/(v₁+v₂))*L₂₁, (v₁/(v₁+v₂))*L₁₂+(v₂/(v₁+v₂))*L₂₂,and so on, where v₁ is the number of upvotes received for comment C₁ andv₂ is the number of upvotes received for comment C₂.

In some embodiments, additional information relating to each comment canbe used to weight the language probability scores in the vectorassociated with each comment. For example, the language probabilityscores in the vector for each comment can be weighted based on the totalnumber of views of each comment. In another example, the languageprobability scores in the vector for each comment can be weighted basedon the number of sub-comments posted in connection with each comment.

Process 400 can continue at 410 by determining a language associatedwith the media content item based at least in part on the combinedvector of language probability scores. In some embodiments, process 400can determine the language associated with the media content item bydetermining the language in the combined vector of language probabilityscores has the maximum or highest language probability score. Incontinuing the example described above in which the language probabilityscores for each comment are weighted by the length of that comment,process 400 can determine which language in the combined vector receivedthe maximum language score when averaged across the selected subset ofcomments associated with the media content item.

In a more particular example, process 400 can associate the mediacontent item with a language identifier that identifies the languagehaving the maximum language score in the combined vector of languageprobability scores.

In some embodiments, prior to determining a language associated with themedia content item, process 400 can use the maximum language score, thefull language probabilities vector, and/or additional features from thecombined vector of language probability scores across the subset ofselected comments associated with the media content item along withadditional indicators to determine the language of the media contentitem.

In some embodiments, these additional indicators can include analyzingthe metadata associated with the media content item. For example,process 400 can generate a vector for information associated with themedia content item, such as the title metadata associated with the mediacontent item and the description metadata associated with the mediacontent item. In a more particular example, text content in the titlemetadata and the description metadata associated with the media contentitem can be filtered to remove particular words (e.g., words deemed tobe machine-generated, auto-generated, or otherwise non-meaningful) orparticular characters (e.g., unidentifiable characters). In turn, theselected portions of the title metadata and/or description metadata canbe classified by one of the machine language classifiers described aboveto return a vector of language probabilities for the title metadataand/or a vector of language probabilities for the description metadata.These additional vectors can be combined or otherwise used in connectionwith the vectors of language probabilities for the subset of selectedcomments to determine the language associated with the media contentitem.

In some embodiments, these additional indicators can also include acategory associated with the media content item. For example, process400 can determine that the media content item is associated with aparticular category, such as a NEWS category, a SPORTS category, a MOVIEcategory, a GAMING category, etc. In response, process 400 can use thecategory information as an indicator of whether the media content itemis likely to include content that is in one language or is likely toinclude content that is in multiple languages. In this example, this canallow process 400 to predict that a video content item in the MOVIEcategory is likely to have only one language associated with the videocontent item, while a video content item in the GAMING category islikely to include audio content that is in multiple languages. Thiscategory indicator along with the vectors described above can be used todetermine the language associated with the media content item.

In some embodiments, these additional indicators can includeinformation, such as popularity information, relating to the mediacontent item. For example, the total number of comments associated withthe media content item (e.g., retrieved from a comments database inresponse to transmitting a media content identifier) can be used as anadditional feature that is augmented with the combined vector oflanguage probability scores for the subset of comments described above.In another example, popularity information, such as the total number ofviews for the media content item can be used as an additional featurethat is augmented with the combined vector of language probabilityscores for the subset of comments described above.

In some embodiments, these additional information can includestatistical information relating to the media content item. For example,the mean and standard deviation of the number of upvotes for thecomments associated with the media content item can be used as anadditional feature that is augmented with the combined vector oflanguage probability scores for the subset of comments described above.In this example, a high standard deviation of the number of upvotes forthe comments associated with the media content item can indicate alesser confidence in the comments-based language identification in thatthere are a number of noisy comments that have received little to noupvotes, while a small standard deviation and a high mean of the numberof upvotes for the comments associated with the media content item canbe used as a confidence indicator for the combined vector of languageprobability scores. In another example, the mean and standard deviationof the length of the comments associated with the media content item canbe used as an additional feature that is augmented with the combinedvector of language probability scores for the subset of commentsdescribed above. In this example, if the mean length of the commentsassociated with the media content item is relatively small (thus,encountering only short comments having less than a particular number ofwords or characters), this can be used as a confidence indicator for thecombined vector of language probability scores in that it may bedifficult to assign a language to the media content item.

Accordingly, any suitable combination of language-related features andmedia content-related features can be used to determine the language ofa media content item.

Referring back to FIG. 4, process 400 can continue at 412 by performingany suitable action based on the language that has been determined forthe media content item, such as recommending a related video or anadditional media content item having the same language identifier as themedia content item, presenting an advertisement having the same languageidentifier as the media content item, determining search resultsresponsive to a search query where the search results have the samelanguage identifier as the media content item, any other suitableaction, and/or any suitable combination thereof. For example, withreference to FIG. 1, in response to process 400 associating mediacontent item 112 with a Spanish language identifier and in response todetermining that a user entered a search query into search element 114,process 400 can present search results that include media content itemsthat are also in Spanish (e.g., media content items associated with aSpanish language identifier). In another example, process 400 canrecommend other media content items having the same Spanish languageidentifier in a user interface (e.g., prior to presenting media contentitem 112, while media content item 112 is being presented, upon thecompletion of media content item 112, etc.).

In a more particular example, process 400 can transmit a request to arecommendation engine for additional media content item based on thelanguage identifier associated with the media content item. In responseto the request, process 400 can provide the user that is consuming themedia content item with related media content items or additional mediacontent items having the same language identifier as the media contentitem, search results responsive to a search query where the searchresults have the same language identifier as the media content item,etc.

In another more particular example, process 400 can transmit a requestto an advertisement server or any other suitable source for advertisingcontent based on the language identifier associated with the mediacontent item. In response to the request, process 400 can present theuser that is consuming the media content item or other media contentitems with an advertisement that is associated with the same language asthe media content item. Such an advertisement can be present before,during, or after the presentation of the media content item. Inconnection with FIG. 1, in response to process 400 associating mediacontent item 112 with a Spanish language identifier, process 400 canretrieve and present an advertisement that is also in Spanish or has aSpanish language identifier in video display element 110 upon thecompletion of playing back media content item 112.

In yet another more particular example, process 400 can use the languageidentifier to set the language of the media player to correspond to thedetermined language. In response, the media player can determine that amedia content item is not in the determined language (e.g., has alanguage identifier corresponding to a different language) and canpresent subtitle information corresponding to the determined languagewhile the media content item is being played back.

In some embodiments, process 400 can perform the comment-based languageidentification described above on multiple media content items, such aseach video in a video database. As a result, some or all of the videosin a database can each be associated with a language identifier or anindication of the language of the content in the media content item.

In some embodiments, the language identifier associated with each of themedia content items can be used along with information relating to theuser to present media content recommendations to the user. For example,in response to receiving an indication that the user of a mediaapplication prefers media content in a particular language (e.g., from alanguage preference indicator), the language identifier associated witheach of the media content items can be used to recommend and/or promotemedia content having a language identifier that matches the languagepreference indicator of the user. In another example, information aboutthe user can be determined (e.g., that the user speaks Spanish). Suchinformation about the user can be determined by, for example, detectingthat a user has accessed a media service using a user account and, inresponse to receiving affirmative consent to review user accountinformation, access user account information (e.g., user languagepreferences). In yet another example, information about the user can bepredicted from user behavior information (e.g., media content items thatthe user has accessed, search terms that the user has inputted, etc.).In response to receiving and/or determining this information about theuser, particular media content items can be recommended to the user. Forexample, in response to determining that the user has entered searchterms into a media searching interface, search results including mediacontent items that are responsive to the search terms can be obtainedand re-ranked using the associated language identifiers. In continuingthis example, search results having language identifiers that correspondto the information about the user (e.g., videos with Spanish languageidentifiers for a user that speaks Spanish) can be promoted to the topof a list of recommended media content items.

It should be noted that, prior to accessing user account information orany other information relating to the user, process 400 can provide theuser with an opportunity to provide affirmative consent or authorizationto perform actions, such as accessing a user profile or obtaining userlanguage preferences. For example, upon loading a media playbackapplication on a user device, the media playback application can promptthe user to provide authorization for accessing language preferenceinformation associated with a user account authenticated on the userdevice. In a more particular example, in response to downloading themedia playback application and/or loading the media playback applicationon the user device, the user can be prompted with a message thatrequests (or requires) that the user provide consent prior to performingthese actions. Additionally or alternatively, in response to installingthe media playback application, the user can be prompted with apermission message that requests (or requires) that the user provideconsent prior to performing these actions.

In some embodiments, at least some of the above described blocks of theprocesses of FIG. 4 can be executed or performed in any order orsequence not limited to the order and sequence shown in and described inconnection with the figures. Also, some of the above blocks of FIG. 4can be executed or performed substantially simultaneously whereappropriate or in parallel to reduce latency and processing times.Additionally or alternatively, some of the above described blocks of theprocesses of FIG. 4 can be omitted.

Although the embodiments disclosed herein have concerned thepresentation of video content, it should be understood that themechanisms described herein can be applied to video-only content,audio-only content, content with a combination of video and audioelements, three-dimensional content, and/or any other suitable mediacontent.

In some embodiments, any suitable computer readable media can be usedfor storing instructions for performing the functions and/or processesherein. For example, in some embodiments, computer readable media can betransitory or non-transitory. For example, non-transitory computerreadable media can include media such as magnetic media (e.g., harddisks, floppy disks, and/or any other suitable magnetic media), opticalmedia (e.g., compact discs, digital video discs, Blu-ray discs, and/orany other suitable optical media), semiconductor media (e.g., flashmemory, electrically programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), and/or any othersuitable semiconductor media), any suitable media that is not fleetingor devoid of any semblance of permanence during transmission, and/or anysuitable tangible media. As another example, transitory computerreadable media can include signals on networks, in wires, conductors,optical fibers, circuits, any suitable media that is fleeting and devoidof any semblance of permanence during transmission, and/or any suitableintangible media.

Accordingly, methods, systems, and media for language identification ofa media content item based on comments are provided.

Although the invention has been described and illustrated in theforegoing illustrative embodiments, it is understood that the presentdisclosure has been made only by way of example, and that numerouschanges in the details of implementation of the invention can be madewithout departing from the spirit and scope of the invention, which islimited only by the claims that follow. Features of the disclosedembodiments can be combined and rearranged in various ways.

What is claimed is:
 1. A method for language identification of mediacontent, the method comprising: obtaining a plurality of commentsassociated with a media content item; selecting a subset of theplurality of comments based on one or more criteria; assigning, for eachcomment in the subset of the plurality of comments, a representation oflanguage probabilities, wherein each component of the representation isassigned a language probability that indicates the likelihood that thecomment includes content in a language from a plurality of languages;combining the representation of language probabilities for each commentin the subset of the plurality of comments to generate a combinedlanguage representation; identifying a language associated with themedia content item based on the combined language representation; andperforming an action based on the identified language that includesdetermining that a second media content item to be presented has alanguage identifier that is different than the identified language andpresenting subtitle information during the presentation of the secondmedia content item, wherein the subtitle information is in theidentified language.
 2. The method of claim 1, wherein selecting thesubset of the plurality of comments based on one or more criteriaincludes removing comments that do not meet a predetermined number ofwords or a predetermined number of characters.
 3. The method of claim 1,further comprising determining a length of each comment in the subset ofthe plurality of comments, wherein the combined language representationis a weighted average of the language probabilities for each of theplurality of languages and across the subset of the plurality ofcomments that is weighted based on the determined length of eachcomment.
 4. The method of claim 1, further comprising determining avoting indication associated with each comment in the subset of theplurality of comments, wherein the combined language representation is aweighted average of the language probabilities for each of the pluralityof languages and across the subset of the plurality of comments that isweighted based on the determined voting indication.
 5. The method ofclaim 1, wherein identifying the language associated with the mediacontent item based on the combined language representation furthercomprises augmenting the combined language representation with anadditional representation of language probabilities corresponding tometadata associated with the media content item.
 6. The method of claim1, wherein identifying the language associated with the media contentitem based on the combined language representation further comprisesaugmenting the combined language representation with media content iteminformation.
 7. The method of claim 6, wherein the media content iteminformation includes a category of the media content item.
 8. The methodof claim 1, wherein performing the action further comprises presentingone or more related media content items in the identified language inresponse to presenting the media content item.
 9. The method of claim 1,wherein performing the action further comprises: transmittinginformation corresponding to the identified language to an advertisementserver; receiving, from the advertisement server, an advertisement thatcorresponds to the identified language; and causing the advertisement tobe presented.
 10. A system for language identification of media content,the system comprising: a hardware processor that is configured to:obtain a plurality of comments associated with a media content item;select a subset of the plurality of comments based on one or morecriteria; assign, for each comment in the subset of the plurality ofcomments, a representation of language probabilities, wherein eachcomponent of the representation is assigned a language probability thatindicates the likelihood that the comment includes content in a languagefrom a plurality of languages; combine the representation of languageprobabilities for each comment in the subset of the plurality ofcomments to generate a combined language representation; identify alanguage associated with the media content item based on the combinedlanguage representation; and perform an action based on the identifiedlanguage that includes determining that a second media content item tobe presented has a language identifier that is different than theidentified language and presenting subtitle information during thepresentation of the second media content item, wherein the subtitleinformation is in the identified language.
 11. The system of claim 10,wherein selecting the subset of the plurality of comments based on oneor more criteria includes removing comments that do not meet apredetermined number of words or a predetermined number of characters.12. The system of claim 10, wherein the hardware processor is furtherconfigured to determine a length of each comment in the subset of theplurality of comments, wherein the combined language representation is aweighted average of the language probabilities for each of the pluralityof languages and across the subset of the plurality of comments that isweighted based on the determined length of each comment.
 13. The systemof claim 10, wherein the hardware processor is further configured todetermine a voting indication associated with each comment in the subsetof the plurality of comments, wherein the combined languagerepresentation is a weighted average of the language probabilities foreach of the plurality of languages and across the subset of theplurality of comments that is weighted based on the determined votingindication.
 14. The system of claim 10, wherein identifying the languageassociated with the media content item based on the combined languagerepresentation further comprises augmenting the combined languagerepresentation with an additional representation of languageprobabilities corresponding to metadata associated with the mediacontent item.
 15. The system of claim 10, wherein identifying thelanguage associated with the media content item based on the combinedlanguage representation further comprises augmenting the combinedlanguage representation with media content item information.
 16. Thesystem of claim 15, wherein the media content item information includesa category of the media content item.
 17. The system of claim 10,wherein performing the action further comprises presenting one or morerelated media content items in the identified language in response topresenting the media content item.
 18. The system of claim 10, whereinperforming the action further comprises: transmitting informationcorresponding to the identified language to an advertisement server;receiving, from the advertisement server, an advertisement thatcorresponds to the identified language; and causing the advertisement tobe presented.
 19. A non-transitory computer-readable medium containingcomputer executable instructions that, when executed by a processor,cause the processor to perform a method for language identification ofmedia content, the method comprising: obtaining a plurality of commentsassociated with a media content item; selecting a subset of theplurality of comments based on one or more criteria; assigning, for eachcomment in the subset of the plurality of comments, a representation oflanguage probabilities, wherein each component of the representation isassigned a language probability that indicates the likelihood that thecomment includes content in a language from a plurality of languages;combining the representation of language probabilities for each commentin the subset of the plurality of comments to generate a combinedlanguage representation; identifying a language associated with themedia content item based on the combined language representation; andperforming an action based on the identified language that includesdetermining that a second media content item to be presented has alanguage identifier that is different than the identified language andpresenting subtitle information during the presentation of the secondmedia content item, wherein the subtitle information is in theidentified language.